County-level socio-environmental factors and obesity prevalence in the United States

Pedro R.V.O. Salerno, Alice Qian, Weichuan Dong, Salil Deo, Khurram Nasir, Sanjay Rajagopalan, Sadeer Al-Kindi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Aims: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. Materials and Methods: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a ‘hold-out’ set of counties and variable importance was evaluated using Random Forest. Results: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). Conclusion: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Original languageEnglish (US)
Pages (from-to)1766-1774
Number of pages9
JournalDiabetes, Obesity and Metabolism
Volume26
Issue number5
DOIs
StatePublished - May 2024

Keywords

  • machine learning
  • obesity prevalence
  • public health
  • Geography
  • United States/epidemiology
  • Prevalence
  • Cross-Sectional Studies
  • Humans
  • Obesity/epidemiology
  • Adult
  • Diabetes Mellitus

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

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